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- W3201612401 abstract "t-SNE (t-distributed Stochastic Neighbor Embedding) is known to be one of the very powerful tools for dimensionality reduction and data visualization. By adopting the student’s t-distribution in the original SNE (Stochastic Neighbor Embedding), t-SNE achieves faster and more stable learning. However, t-SNE still poses computational complexity due to its dependence on KL-divergence. Our goal is to extend t-SNE in a natural way by the framework of information geometry. Our generalized t-SNE can outperform the original t-SNE with a well-chosen set of parameters. Furthermore, the experimental results for MNIST, Fashion MNIST and COIL-20, show that our generalized t-SNE outperforms the original t-SNE." @default.
- W3201612401 created "2021-09-27" @default.
- W3201612401 creator A5065015874 @default.
- W3201612401 date "2021-01-01" @default.
- W3201612401 modified "2023-09-23" @default.
- W3201612401 title "Generalized t-SNE Through the Lens of Information Geometry" @default.
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- W3201612401 doi "https://doi.org/10.1109/access.2021.3113397" @default.
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